Uncertainty is described as the ability of a model to convey the ambiguity in the decision [1]}. Deep learning based classifiers typically return the class with the highest softmax output as the result, even if there is a small margin. There is a need for the end-users to be alerted when a model is uncertain to avoid potential wrong decisions. There are two sources of uncertainty in a system - aleatoric and epistemic [2]}, [1]}. Aleatoric uncertainty or doubt is the inherent uncertainty in the system arising from the modeling process e.g., stochastic behavior. On the other hand, epistemic uncertainty or ambiguity arises due to limited data leading to lower knowledge of the system. The former is inherent and can not be changed for a given model. The latter can be reduced effectively with larger training data and ensuring that new kinds of data (or adversary) are identified.
